Research

High-performance data structures are at the heart of many applications. In our PLDI 2016 paper we describe how to generate complex data structure implementations from high-level specifications. Cozy (our implementation of these techniques) is available online.

I am currently working on extensions to Cozy so that it can handle a much wider class of data structures.

My group is working on formal verification techniques for a large real-world system: the Clinical Neutron Therapy System at the UW Medical Center. The CNTS is used for actual cancer treatments, so we want a high degree of confidence that all the code is correct!

Verifying all the CNTS code is challenging since it is built on many different technologies. Our SNAPL 2015 paper describes the need to combine many disparate verification strategies into a cohesive piece of evidence. In our CAV 2016 paper we describe how we actually achieved this for (parts of) CNTS.

Type Error Diagnosis

Many developers have horror stories of struggling to diagnose type errors in type-inferred languages like ML and Haskell. Recent research promises far more accurate error messages, but the techniques are difficult to implement and slow to run.

Our OOPSLA 2016 paper describes how to achieve comparable quality at substantially lower run-time cost. Even better, our technique can be implemented with only small modifications to a compiler's existing type inference algorithm!

There is also a tech report on this research that expands some details in the OOPSLA paper. In particular, Appendix C gives advice for compiler writers and future researchers. Note that this is my own version of the paper and has not been peer-reviewed. (Last updated 10 October, 2016.)